COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR BREAST CANCER DETECTION

AFOLAYAN, JESUTOFUNMI ONAOPE (2021) COMPARATIVE ANALYSIS OF MACHINE LEARNING TECHNIQUES FOR BREAST CANCER DETECTION. Masters thesis, Landmark University, Omu Aran, Kwara State.

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Abstract

Death from cancer is one of humanity's biggest problem, though there are many ways of stopping it before it occurs, there are still no cure forms of cancer. Due to recent population growth in clinical research, effective diagnosis of cancer is significant. The rate of death from breast cancer is increasing significantly with the rapid growth of the population. Cancer of the breast is one of the major cancer-related deaths amongst women globally. Survival rates differ across the numerous health treatments provided, comprising of surgery, chemotherapy, surgical procedures, and radiation treatment. To facilitate quick treatment and also to achieve more reliable outcomes, data analysis approaches employed for the detection and treatment of cancer of the breast have to be improved.The aim of this study is to carry out a comparative analysis of machine learning techniques for breast cancer detection. This study was analyzed using The Wisconsin breast cancer datasets from an online UCI machine-learning repository.Feature selection was carried out through Particle Swarm Optimization algorithm (PSO), this algorithm helped pick relevant features from the raw dataset to eliminate and reduce noises for a better outcome and then a reduced dataset was achieved.Three(3) machine learning algorithms for classification was used namely: The support vector machine (SVM), artificial neural networks (ANNs), and decision tree (DT), for classification purpose, and these classifiers were used for further analysis on the reduced dataset to simulate the model

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